Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial
Introduction As surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise and objective risk stratification tool holds the potential to mitigate these complications by early identification of high-risk patients. Moreover, it co...
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BMJ Publishing Group
2024-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/12/e086769.full |
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| author | Marcus Ong Hairil Rizal Abdullah Ecosse Lamoureux Elaine Lum Gek Hsiang Lim Tan Pei Yi Brenda Celestine Loh |
| author_facet | Marcus Ong Hairil Rizal Abdullah Ecosse Lamoureux Elaine Lum Gek Hsiang Lim Tan Pei Yi Brenda Celestine Loh |
| author_sort | Marcus Ong |
| collection | DOAJ |
| description | Introduction As surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise and objective risk stratification tool holds the potential to mitigate these complications by early identification of high-risk patients. Moreover, it could address the escalating costs from resource misallocation. In Singapore General Hospital (SGH), we introduced the Combined Assessment of Risk Encountered in Surgery-Machine Learning (CARES-ML) in June 2023, focusing on predicting 30-day postoperative mortality and the need for post-surgery intensive care unit (ICU) stays. The IMAGINATIVE Trial aims to evaluate the efficacy of such systems in a large academic medical centre.Methods and analysis This study adopts type 1 effectiveness-implementation study design within a randomised controlled trial framework. Patients will be randomly assigned in a 1:1 ratio to either the CARES-guided group (unblinded to risk level) or the unguided group (blinded to the risk level). A total of 9200 patients will be enrolled in the study, with the inclusion criteria encompassing individuals aged 21–100 years old undergoing elective surgeries except for neurology and cardiology surgeries at SGH. The primary outcome is to evaluate the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS) algorithm in improving perioperative mortality rates when integrated into the clinical workflow.Ethics and dissemination The study has been approved by the SingHealth Centralised Institutional Review Board (CIRB Ref: 2023:2114) and is registered on ClinicalTrials.gov (trial number: NCT05809232). All patients will sign an informed consent form before recruitment and translators will be made available to non-English-speaking participants. This study is funded by the National Medical Research Council, Singapore (HCSAINV22jul-0002) and the findings will be published in peer-reviewed journals and presented at academic conferences.Trial registration number NCT05809232. |
| format | Article |
| id | doaj-art-11744187e8224156a5bec7dbead130a2 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-11744187e8224156a5bec7dbead130a22025-01-14T10:00:08ZengBMJ Publishing GroupBMJ Open2044-60552024-12-01141210.1136/bmjopen-2024-086769Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trialMarcus Ong0Hairil Rizal Abdullah1Ecosse Lamoureux2Elaine Lum3Gek Hsiang Lim4Tan Pei Yi Brenda5Celestine Loh6Department of Emergency Medicine, Singapore General Hospital, SingaporeDepartment of Anesthesiology, Singapore General Hospital, SingaporeHealth Services and Systems Research, Duke-NUS Medical School, SingaporeHealth Services and Systems Research, Duke-NUS Medical School, SingaporeHealth Services Research Unit, Singapore General Hospital, SingaporeDepartment of Anesthesiology, Singapore General Hospital, SingaporeDuke-NUS Medical School, SingaporeIntroduction As surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise and objective risk stratification tool holds the potential to mitigate these complications by early identification of high-risk patients. Moreover, it could address the escalating costs from resource misallocation. In Singapore General Hospital (SGH), we introduced the Combined Assessment of Risk Encountered in Surgery-Machine Learning (CARES-ML) in June 2023, focusing on predicting 30-day postoperative mortality and the need for post-surgery intensive care unit (ICU) stays. The IMAGINATIVE Trial aims to evaluate the efficacy of such systems in a large academic medical centre.Methods and analysis This study adopts type 1 effectiveness-implementation study design within a randomised controlled trial framework. Patients will be randomly assigned in a 1:1 ratio to either the CARES-guided group (unblinded to risk level) or the unguided group (blinded to the risk level). A total of 9200 patients will be enrolled in the study, with the inclusion criteria encompassing individuals aged 21–100 years old undergoing elective surgeries except for neurology and cardiology surgeries at SGH. The primary outcome is to evaluate the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS) algorithm in improving perioperative mortality rates when integrated into the clinical workflow.Ethics and dissemination The study has been approved by the SingHealth Centralised Institutional Review Board (CIRB Ref: 2023:2114) and is registered on ClinicalTrials.gov (trial number: NCT05809232). All patients will sign an informed consent form before recruitment and translators will be made available to non-English-speaking participants. This study is funded by the National Medical Research Council, Singapore (HCSAINV22jul-0002) and the findings will be published in peer-reviewed journals and presented at academic conferences.Trial registration number NCT05809232.https://bmjopen.bmj.com/content/14/12/e086769.full |
| spellingShingle | Marcus Ong Hairil Rizal Abdullah Ecosse Lamoureux Elaine Lum Gek Hsiang Lim Tan Pei Yi Brenda Celestine Loh Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial BMJ Open |
| title | Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial |
| title_full | Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial |
| title_fullStr | Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial |
| title_full_unstemmed | Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial |
| title_short | Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial |
| title_sort | protocol for the impact of machine learning based clinician decision support algorithims in perioperative care imaginative in singapore general hospital a large prospective randomised controlled trial |
| url | https://bmjopen.bmj.com/content/14/12/e086769.full |
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